IDEAS home Printed from https://ideas.repec.org/a/bpj/bejtec/v18y2018i1p20n3.html
   My bibliography  Save this article

Stochastic Logistic Model of the Global Financial Leverage

Author

Listed:
  • Smirnov Alexander D.

    (Department of Applied Macroeconomics, National Research University Higher School of Economics, Moscow, Russia)

Abstract

Debt, as one of basic human relations, has profound effects on economic growth. Debt accumulation in the global economy was modeled by the stochastic logistic equation reflecting causality between leverage and its rate of change. The model, identifying interactions and feedbacks in aggregate behaviour of creditors and borrowers, addressed various issues of macrofinancial stability. Qualitatively diverse patterns, including the Wicksellian (normal) market, the Minsky financial bubbles and the Fisherian debt-deflation, were discerned by appropriate combinations of rates of return, spreads and leverage. The Kolmogorov-Fokker-Plank equation was used to find out the stationary gamma distribution of leverage that was instrumental for the evaluation of appropriate failure and survival functions. Two patterns corresponding to different forms of a stationary gamma distribution were recognized in the long run leverage dynamics and were simulated as scenarios of a possible system evolution. In particular, empirically parameterized asymptotical distribution indicated excessive leverage and unsustainable global debt accumulation. It underlined the necessity of comprehensive reforms aiming to decrease uncertainty, debt and leverage. Assuming these reforms were successfully implemented, global leverage distributions would have converged in the long run to a peaked gamma distribution with the mode identical to the anchor leverage. The latter corresponded to a balanced long run debt demand and supply, hence to fairly evaluated financial assets fully collateralized by real resources. A particular case of macrofinancial Tobin’s q-coefficients following the Ornstein-Ulenbeck process was studied to evaluate a reasonable range of squeezing the bloated world finance. The model was verified on data published by the IMF in Global Financial Stability Reports for the period 2003–2013.

Suggested Citation

  • Smirnov Alexander D., 2018. "Stochastic Logistic Model of the Global Financial Leverage," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 18(1), pages 1-20, January.
    • Handle: RePEc:bpj:bejtec:v:18:y:2018:i:1:p:20:n:3
    DOI: 10.1515/bejte-2016-0009
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/bejte-2016-0009
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/bejte-2016-0009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Raymond W. Goldsmith, 1959. "Financial Structure and Development as a Subject for International Comparative Study," NBER Chapters, in: The Comparative Study of Economic Growth and Structure, pages 114-123, National Bureau of Economic Research, Inc.
    2. Ole Peters, 2011. "Optimal leverage from non-ergodicity," Quantitative Finance, Taylor & Francis Journals, vol. 11(11), pages 1593-1602.
    3. Sorin Solomon & Peter Richmond, 2000. "Stability of Pareto-Zipf Law in Non-Stationary Economies," Papers cond-mat/0012479, arXiv.org, revised Jan 2001.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Viktor Stojkoski & Trifce Sandev & Lasko Basnarkov & Ljupco Kocarev & Ralf Metzler, 2020. "Generalised geometric Brownian motion: Theory and applications to option pricing," Papers 2011.00312, arXiv.org.
    2. Yingying Zhou & Zhuoqing Fang & Nan Li & Xueyan Wu & Yuehan Du & Zonghan Liu, 2019. "How Does Financial Development Affect Reductions in Carbon Emissions in High-Energy Industries?—A Perspective on Technological Progress," IJERPH, MDPI, vol. 16(17), pages 1-25, August.
    3. Mimkes, Jürgen, 2010. "Stokes integral of economic growth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1665-1676.
    4. Solomon, Sorin & Richmond, Peter, 2001. "Power laws of wealth, market order volumes and market returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 188-197.
    5. J. Doyne Farmer & Spyros Skouras, 2013. "An ecological perspective on the future of computer trading," Quantitative Finance, Taylor & Francis Journals, vol. 13(3), pages 325-346, February.
    6. G. Yaari & D. Stauffer & S. Solomon, 2008. "Intermittency and Localization," Papers 0802.3541, arXiv.org, revised Mar 2008.
    7. Navarro-Barrientos, Jesús Emeterio & Cantero-Álvarez, Rubén & Matias Rodrigues, João F. & Schweitzer, Frank, 2008. "Investments in random environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2035-2046.
    8. Ausloos, M. & Bronlet, Ph., 2003. "Strategy for investments from Zipf law(s)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 30-37.
    9. Anwar, Amar & Iwasaki, Ichiro, 2022. "The Finance–Growth Nexus inAsia : A Meta-Analytic Approach," CEI Working Paper Series 2022-03, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    10. Mariam Thalos & Oliver Richardson, 2014. "Capitalization in the St. Petersburg game," Politics, Philosophy & Economics, , vol. 13(3), pages 292-313, August.
    11. Gonca Atici, 2018. "Islamic (Participation) Banking and Economic Growth: Empirical Focus on Turkey," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 8(11), pages 1354-1364, November.
    12. Bell, Peter Newton, 2014. "Properties of time averages in a risk management simulation," MPRA Paper 55803, University Library of Munich, Germany.
    13. Stefan Thurner & J. Doyne Farmer & John Geanakoplos, 2012. "Leverage causes fat tails and clustered volatility," Quantitative Finance, Taylor & Francis Journals, vol. 12(5), pages 695-707, February.
    14. Jagadish Prasad Bist, 2018. "Financial development and economic growth: Evidence from a panel of 16 African and non-African low-income countries," Cogent Economics & Finance, Taylor & Francis Journals, vol. 6(1), pages 1449780-144, January.
    15. Ole Peters & Murray Gell-Mann, 2014. "Evaluating gambles using dynamics," Papers 1405.0585, arXiv.org, revised Jun 2015.
    16. Simon Gluzman, 2023. "Market Crashes and Time-Translation Invariance," FinTech, MDPI, vol. 2(2), pages 1-27, March.
    17. Matej Uhr'in & Gustav v{S}ourek & Ondv{r}ej Hub'av{c}ek & Filip v{Z}elezn'y, 2021. "Optimal sports betting strategies in practice: an experimental review," Papers 2107.08827, arXiv.org.
    18. Mihail Turlakov, 2016. "Leverage and Uncertainty," Papers 1612.07194, arXiv.org.
    19. Aleksejus Kononovicius & Valentas Daniunas, 2013. "Agent-based and macroscopic modeling of the complex socio-economic systems," Papers 1303.3693, arXiv.org, revised Apr 2013.
    20. Rosella Giacometti & Sergio Ortobelli & Tomáš Tichý, 2015. "Portfolio Selection with Uncertainty Measures Consistent with Additive Shifts," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(1), pages 3-16.

    More about this item

    Keywords

    leverage; logistic equation; debt collateral; gamma distribution; hazard function;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • G1 - Financial Economics - - General Financial Markets
    • N2 - Economic History - - Financial Markets and Institutions

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:bejtec:v:18:y:2018:i:1:p:20:n:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.